Research on financial irregularities identification: a machine learning perspective

被引：0
作者：
Chai K.-C. [1 ]
Zhu J. [1 ]
Yang Y. [1 ]
Lan H.-R. [1 ]
Ou Y.-L. [1 ]
Li Q. [1 ]
机构：
[1] Business School, Guilin University of Electronic Technology, Guangxi Province, Guilin
来源：
International Journal of Internet Manufacturing and Services | 2022年 / 8卷 / 04期
关键词：
China; financial irregularities identification; machine learning; SMOTE algorithm;
D O I：
10.1504/IJIMS.2022.10050659
中图分类号：
学科分类号：
摘要：
In the era of big data, data-driven analytics can generate many meaningful insights. With the gradual maturity of artificial intelligence (AI) algorithm, it can help solve problems that are difficult to identify in the financial field. Through theoretical analysis, this paper constructs feature engineering of multiple internal and external factors that affect corporate financial irregularities, and then automatically identifies Chinese listed companies with financial irregularities based on machine learning algorithm. In this paper, we verify the effectiveness of SMOTE algorithm in improving the imbalance data of financial irregularities of Chinese listed companies and use LightGBM algorithm to sort the ten factors of characteristic importance of financial irregularities of Chinese listed companies. This paper provides a new way to detect financial irregularities for financial regulatory authorities and a paradigm for the applying of AI in the financial field. Copyright © 2022 Inderscience Enterprises Ltd.
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收藏
页码：383 / 399
页数：16
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